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"source": [
"import sys\n",
"import os\n",
"sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '../..')))\n",
"\n",
"# Path Configuration\n",
"from tools.preprocess import *\n",
"\n",
"# Processing context\n",
"trait = \"Endometriosis\"\n",
"cohort = \"GSE120103\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Endometriosis\"\n",
"in_cohort_dir = \"../../input/GEO/Endometriosis/GSE120103\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Endometriosis/GSE120103.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Endometriosis/gene_data/GSE120103.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Endometriosis/clinical_data/GSE120103.csv\"\n",
"json_path = \"../../output/preprocess/Endometriosis/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "9f7c01bd",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
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"metadata": {
"execution": {
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Background Information:\n",
"!Series_title\t\"Homo sapiens whole genome expression microarray of endometrium obtained from fertile and Infertile women with stage IV ovarian endometriosis and without endometriosis\"\n",
"!Series_summary\t\"The hypothesis that male michrochimerism in eutopic endometrium is a factor for endometriosis, as indicated by indirect evidence was examined in endometrial samples from control (Group 1) and stage IV ovarian endometriosis (Group 2), either fertile (Group 1A and 2A) or Infertile (Group 1B and 2B) pateints.\"\n",
"!Series_summary\t\"6 coding and 10 non-coding genes showed bi-modal pattern of expression characterised by low expression in samples obtained from fertile patients and high expressions in infertile patients. Several coding and non-coding MSY-linked genes displayed michrochimerism in form of presence of their respective DNA inserts along with their microarray-detectable expression in endometrium irrespective of fertility history and disease.\"\n",
"!Series_overall_design\t\"Whole genome expression arrays of endometrial total RNA obtained from endometrium of women without endometriosis (Group 1) and with stage IV ovarian endometriosis (Group 2), either fertile (Group 1A and 2A) or Infertile (Group 1B and 2B).\"\n",
"Sample Characteristics Dictionary:\n",
"{0: ['gender: Female'], 1: ['sample group: Disease free Endometrium of fertile women', 'sample group: Endometrium from Stage IV Ovarian Endometriosis of fertile women', 'sample group: Disease free Endometrium of Infertile women', 'sample group: Endometrium from Stage IV Ovarian Endometriosis of Infertile women'], 2: ['tissue: Endometrium']}\n"
]
}
],
"source": [
"from tools.preprocess import *\n",
"# 1. Identify the paths to the SOFT file and the matrix file\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. Read the matrix file to obtain background information and sample characteristics data\n",
"background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']\n",
"clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']\n",
"background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
"\n",
"# 3. Obtain the sample characteristics dictionary from the clinical dataframe\n",
"sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
"\n",
"# 4. Explicitly print out all the background information and the sample characteristics dictionary\n",
"print(\"Background Information:\")\n",
"print(background_info)\n",
"print(\"Sample Characteristics Dictionary:\")\n",
"print(sample_characteristics_dict)\n"
]
},
{
"cell_type": "markdown",
"id": "695fdc74",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Clinical Features Preview:\n",
"{0: [0.0], 1: [1.0], 2: [0.0], 3: [1.0]}\n",
"Clinical features saved to ../../output/preprocess/Endometriosis/clinical_data/GSE120103.csv\n"
]
}
],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on the series description, this appears to be a whole genome expression microarray dataset,\n",
"# so we can confidently set this to True\n",
"is_gene_available = True\n",
"\n",
"# 2.1 Data Availability\n",
"# For the trait (endometriosis), the relevant key is 1, which indicates sample group information\n",
"trait_row = 1 \n",
"\n",
"# For age, there's no information available in the sample characteristics\n",
"age_row = None\n",
"\n",
"# For gender, we can see that gender information is available at key 0,\n",
"# but it shows only one value \"Female\", so it's a constant feature and should be considered unavailable\n",
"gender_row = None \n",
"\n",
"# 2.2 Data Type Conversion\n",
"def convert_trait(value):\n",
" \"\"\"Convert sample group to binary values for endometriosis trait.\"\"\"\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon if present\n",
" if isinstance(value, str) and ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" # Based on the values, we need to determine who has endometriosis\n",
" if \"Stage IV Ovarian Endometriosis\" in value:\n",
" return 1 # Has endometriosis\n",
" elif \"Disease free Endometrium\" in value:\n",
" return 0 # Does not have endometriosis\n",
" else:\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age to continuous values.\"\"\"\n",
" # This function is included for completeness but won't be used as age data is not available\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon if present\n",
" if isinstance(value, str) and ':' in value:\n",
" value = value.split(':', 1)[1].strip()\n",
" \n",
" try:\n",
" return float(value)\n",
" except:\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"Convert gender to binary values.\"\"\"\n",
" # This function is included for completeness but won't be used as gender data is not available\n",
" if pd.isna(value) or value is None:\n",
" return None\n",
" \n",
" # Extract the value after the colon if present\n",
" if isinstance(value, str) and ':' in value:\n",
" value = value.split(':', 1)[1].strip().lower()\n",
" \n",
" if value == \"female\":\n",
" return 0\n",
" elif value == \"male\":\n",
" return 1\n",
" else:\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Determine if trait data is available based on whether trait_row is None\n",
"is_trait_available = trait_row is not None\n",
"\n",
"# Call the validation function\n",
"validate_and_save_cohort_info(\n",
" is_final=False,\n",
" cohort=cohort,\n",
" info_path=json_path,\n",
" is_gene_available=is_gene_available,\n",
" is_trait_available=is_trait_available\n",
")\n",
"\n",
"# 4. Clinical Feature Extraction\n",
"if trait_row is not None:\n",
" # Create a proper DataFrame for the clinical data\n",
" # Using a more appropriate structure for geo_select_clinical_features\n",
" \n",
" # Initialize sample characteristics dictionary with proper structure\n",
" sample_characteristics = {\n",
" 0: ['gender: Female'], \n",
" 1: ['sample group: Disease free Endometrium of fertile women', \n",
" 'sample group: Endometrium from Stage IV Ovarian Endometriosis of fertile women', \n",
" 'sample group: Disease free Endometrium of Infertile women', \n",
" 'sample group: Endometrium from Stage IV Ovarian Endometriosis of Infertile women'], \n",
" 2: ['tissue: Endometrium']\n",
" }\n",
" \n",
" # Create a clinical DataFrame with appropriate format for geo_select_clinical_features\n",
" # Each row represents one characteristic (like trait, gender)\n",
" clinical_data_dict = {}\n",
" for key, values in sample_characteristics.items():\n",
" clinical_data_dict[key] = values\n",
" \n",
" clinical_data = pd.DataFrame.from_dict(clinical_data_dict, orient='index')\n",
" \n",
" # Extract clinical features\n",
" selected_clinical_df = geo_select_clinical_features(\n",
" clinical_df=clinical_data,\n",
" trait=trait,\n",
" trait_row=trait_row,\n",
" convert_trait=convert_trait,\n",
" age_row=age_row,\n",
" convert_age=convert_age,\n",
" gender_row=gender_row,\n",
" convert_gender=convert_gender\n",
" )\n",
" \n",
" # Preview the results\n",
" preview = preview_df(selected_clinical_df)\n",
" print(\"Clinical Features Preview:\")\n",
" print(preview)\n",
" \n",
" # Create directory if it doesn't exist\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" \n",
" # Save the clinical features to CSV\n",
" selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n",
" print(f\"Clinical features saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "da277219",
"metadata": {},
"source": [
"### Step 3: Gene Data Extraction"
]
},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Found data marker at line 66\n",
"Header line: \"ID_REF\"\t\"GSM3393491\"\t\"GSM3393492\"\t\"GSM3393493\"\t\"GSM3393494\"\t\"GSM3393495\"\t\"GSM3393496\"\t\"GSM3393497\"\t\"GSM3393498\"\t\"GSM3393499\"\t\"GSM3393500\"\t\"GSM3393501\"\t\"GSM3393502\"\t\"GSM3393503\"\t\"GSM3393504\"\t\"GSM3393505\"\t\"GSM3393506\"\t\"GSM3393507\"\t\"GSM3393508\"\t\"GSM3393509\"\t\"GSM3393510\"\t\"GSM3393511\"\t\"GSM3393512\"\t\"GSM3393513\"\t\"GSM3393514\"\t\"GSM3393515\"\t\"GSM3393516\"\t\"GSM3393517\"\t\"GSM3393518\"\t\"GSM3393519\"\t\"GSM3393520\"\t\"GSM3393521\"\t\"GSM3393522\"\t\"GSM3393523\"\t\"GSM3393524\"\t\"GSM3393525\"\t\"GSM3393526\"\n",
"First data line: \"(+)E1A_r60_1\"\t0.2933545\t0.012980461\t-0.23412466\t-0.7159295\t-0.012980461\t-1.7718949\t-2.2321372\t-1.1389613\t0.4525237\t-1.8300767\t-2.9251266\t-2.3127236\t-1.1785226\t-2.9045892\t-0.2190404\t-1.5515742\t-2.569182\t-1.8102136\t14.862822\t14.86257\t14.874119\t14.430498\t14.441908\t14.338968\t14.85437\t-2.4582553\t-2.5016837\t-0.20707464\t0.17501879\t0.013408184\t0.5477576\t6.3862505\t5.2938547\t6.3140154\t0.4224906\t3.351428\n",
"Index(['(+)E1A_r60_1', '(+)E1A_r60_3', '(+)E1A_r60_a104', '(+)E1A_r60_a107',\n",
" '(+)E1A_r60_a135', '(+)E1A_r60_a20', '(+)E1A_r60_a22', '(+)E1A_r60_a97',\n",
" '(+)E1A_r60_n11', '(+)E1A_r60_n9', '(+)eQC-39', '(+)eQC-40',\n",
" '(+)eQC-41', '(+)eQC-42', '(-)3xSLv1', 'A_23_P100001', 'A_23_P100011',\n",
" 'A_23_P100022', 'A_23_P100056', 'A_23_P100074'],\n",
" dtype='object', name='ID')\n"
]
}
],
"source": [
"# 1. Get the file paths for the SOFT file and matrix file\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# 2. First, let's examine the structure of the matrix file to understand its format\n",
"import gzip\n",
"\n",
"# Peek at the first few lines of the file to understand its structure\n",
"with gzip.open(matrix_file, 'rt') as file:\n",
" # Read first 100 lines to find the header structure\n",
" for i, line in enumerate(file):\n",
" if '!series_matrix_table_begin' in line:\n",
" print(f\"Found data marker at line {i}\")\n",
" # Read the next line which should be the header\n",
" header_line = next(file)\n",
" print(f\"Header line: {header_line.strip()}\")\n",
" # And the first data line\n",
" first_data_line = next(file)\n",
" print(f\"First data line: {first_data_line.strip()}\")\n",
" break\n",
" if i > 100: # Limit search to first 100 lines\n",
" print(\"Matrix table marker not found in first 100 lines\")\n",
" break\n",
"\n",
"# 3. Now try to get the genetic data with better error handling\n",
"try:\n",
" gene_data = get_genetic_data(matrix_file)\n",
" print(gene_data.index[:20])\n",
"except KeyError as e:\n",
" print(f\"KeyError: {e}\")\n",
" \n",
" # Alternative approach: manually extract the data\n",
" print(\"\\nTrying alternative approach to read the gene data:\")\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" # Find the start of the data\n",
" for line in file:\n",
" if '!series_matrix_table_begin' in line:\n",
" break\n",
" \n",
" # Read the headers and data\n",
" import pandas as pd\n",
" df = pd.read_csv(file, sep='\\t', index_col=0)\n",
" print(f\"Column names: {df.columns[:5]}\")\n",
" print(f\"First 20 row IDs: {df.index[:20]}\")\n",
" gene_data = df\n"
]
},
{
"cell_type": "markdown",
"id": "bfbf3528",
"metadata": {},
"source": [
"### Step 4: Gene Identifier Review"
]
},
{
"cell_type": "code",
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"source": [
"# Review gene identifiers in the data\n",
"# The identifiers such as 'A_23_P100001', 'A_23_P100011', etc. appear to be Agilent microarray probe IDs\n",
"# rather than standard human gene symbols. These will need to be mapped to gene symbols.\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "5987223f",
"metadata": {},
"source": [
"### Step 5: Gene Annotation"
]
},
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{
"name": "stdout",
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"text": [
"Gene annotation preview:\n",
"{'ID': ['A_23_P100001', 'A_23_P100011', 'A_23_P100022', 'A_23_P100056', 'A_23_P100074'], 'SPOT_ID': ['A_23_P100001', 'A_23_P100011', 'A_23_P100022', 'A_23_P100056', 'A_23_P100074'], 'CONTROL_TYPE': ['FALSE', 'FALSE', 'FALSE', 'FALSE', 'FALSE'], 'REFSEQ': ['NM_207446', 'NM_005829', 'NM_014848', 'NM_194272', 'NM_020371'], 'GB_ACC': ['NM_207446', 'NM_005829', 'NM_014848', 'NM_194272', 'NM_020371'], 'GENE': [400451.0, 10239.0, 9899.0, 348093.0, 57099.0], 'GENE_SYMBOL': ['FAM174B', 'AP3S2', 'SV2B', 'RBPMS2', 'AVEN'], 'GENE_NAME': ['family with sequence similarity 174, member B', 'adaptor-related protein complex 3, sigma 2 subunit', 'synaptic vesicle glycoprotein 2B', 'RNA binding protein with multiple splicing 2', 'apoptosis, caspase activation inhibitor'], 'UNIGENE_ID': ['Hs.27373', 'Hs.632161', 'Hs.21754', 'Hs.436518', 'Hs.555966'], 'ENSEMBL_ID': ['ENST00000557398', nan, 'ENST00000557410', 'ENST00000300069', 'ENST00000306730'], 'TIGR_ID': [nan, nan, nan, nan, nan], 'ACCESSION_STRING': ['ref|NM_207446|ens|ENST00000557398|ens|ENST00000553393|ens|ENST00000327355', 'ref|NM_005829|ref|NM_001199058|ref|NR_023361|ref|NR_037582', 'ref|NM_014848|ref|NM_001167580|ens|ENST00000557410|ens|ENST00000330276', 'ref|NM_194272|ens|ENST00000300069|gb|AK127873|gb|AK124123', 'ref|NM_020371|ens|ENST00000306730|gb|AF283508|gb|BC010488'], 'CHROMOSOMAL_LOCATION': ['chr15:93160848-93160789', 'chr15:90378743-90378684', 'chr15:91838329-91838388', 'chr15:65032375-65032316', 'chr15:34158739-34158680'], 'CYTOBAND': ['hs|15q26.1', 'hs|15q26.1', 'hs|15q26.1', 'hs|15q22.31', 'hs|15q14'], 'DESCRIPTION': ['Homo sapiens family with sequence similarity 174, member B (FAM174B), mRNA [NM_207446]', 'Homo sapiens adaptor-related protein complex 3, sigma 2 subunit (AP3S2), transcript variant 1, mRNA [NM_005829]', 'Homo sapiens synaptic vesicle glycoprotein 2B (SV2B), transcript variant 1, mRNA [NM_014848]', 'Homo sapiens RNA binding protein with multiple splicing 2 (RBPMS2), mRNA [NM_194272]', 'Homo sapiens apoptosis, caspase activation inhibitor (AVEN), mRNA [NM_020371]'], 'GO_ID': ['GO:0016020(membrane)|GO:0016021(integral to membrane)', 'GO:0005794(Golgi apparatus)|GO:0006886(intracellular protein transport)|GO:0008565(protein transporter activity)|GO:0016020(membrane)|GO:0016192(vesicle-mediated transport)|GO:0030117(membrane coat)|GO:0030659(cytoplasmic vesicle membrane)|GO:0031410(cytoplasmic vesicle)', 'GO:0001669(acrosomal vesicle)|GO:0006836(neurotransmitter transport)|GO:0016020(membrane)|GO:0016021(integral to membrane)|GO:0022857(transmembrane transporter activity)|GO:0030054(cell junction)|GO:0030672(synaptic vesicle membrane)|GO:0031410(cytoplasmic vesicle)|GO:0045202(synapse)', 'GO:0000166(nucleotide binding)|GO:0003676(nucleic acid binding)', 'GO:0005515(protein binding)|GO:0005622(intracellular)|GO:0005624(membrane fraction)|GO:0006915(apoptosis)|GO:0006916(anti-apoptosis)|GO:0012505(endomembrane system)|GO:0016020(membrane)'], 'SEQUENCE': ['ATCTCATGGAAAAGCTGGATTCCTCTGCCTTACGCAGAAACACCCGGGCTCCATCTGCCA', 'TCAAGTATTGGCCTGACATAGAGTCCTTAAGACAAGCAAAGACAAGCAAGGCAAGCACGT', 'ATGTCGGCTGTGGAGGGTTAAAGGGATGAGGCTTTCCTTTGTTTAGCAAATCTGTTCACA', 'CCCTGTCAGATAAGTTTAATGTTTAGTTTGAGGCATGAAGAAGAAAAGGGTTTCCATTCT', 'GACCAGCCAGTTTACAAGCATGTCTCAAGCTAGTGTGTTCCATTATGCTCACAGCAGTAA']}\n"
]
}
],
"source": [
"# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
"gene_annotation = get_gene_annotation(soft_file)\n",
"\n",
"# 2. Use the 'preview_df' function from the library to preview the data and print out the results.\n",
"print(\"Gene annotation preview:\")\n",
"print(preview_df(gene_annotation))\n"
]
},
{
"cell_type": "markdown",
"id": "4a136139",
"metadata": {},
"source": [
"### Step 6: Gene Identifier Mapping"
]
},
{
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Gene mapping preview:\n",
"{'ID': ['A_23_P100001', 'A_23_P100011', 'A_23_P100022', 'A_23_P100056', 'A_23_P100074'], 'Gene': ['FAM174B', 'AP3S2', 'SV2B', 'RBPMS2', 'AVEN']}\n",
"Gene expression data shape after mapping: (17678, 36)\n",
"First few gene symbols after mapping:\n",
"Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2LD1', 'A2M', 'A2ML1', 'A4GALT', 'A4GNT',\n",
" 'AAAS', 'AACS'],\n",
" dtype='object', name='Gene')\n",
"Gene expression data preview:\n",
"{'GSM3393491': [-1.3306704, -1.0435967, 1.993511226, 2.275426, 0.53070736], 'GSM3393492': [-1.4305425, -0.54736996, 0.54174758, 0.47634304, 0.3492117], 'GSM3393493': [-1.7391768, -0.5067458, 2.64482165, 0.9172963, -0.19257355], 'GSM3393494': [-0.65010595, 0.27574682, -1.99191613, 1.6102637, 0.24081898], 'GSM3393495': [-0.7661872, -1.1883221, 0.8087396800000001, 2.1443443, 0.3116703], 'GSM3393496': [0.43882227, -0.002605438, -1.50206663, -0.15116894, 0.49943542], 'GSM3393497': [0.25899267, -0.015143395, -2.4413662, -0.30618966, 0.19794655], 'GSM3393498': [0.39300776, 0.77118206, -0.94638018, -0.44590962, 0.24827003], 'GSM3393499': [0.40918016, 0.78752136, 0.5837469200000001, -0.38014424, 0.2881632], 'GSM3393500': [-0.019157887, 0.002605438, -1.46312666, 1.7641228, -0.04834938], 'GSM3393501': [0.21949339, 0.46449852, -1.9212579399999998, -0.15999234, 1.18081], 'GSM3393502': [0.3701806, 0.17487144, 1.37367252, -0.22474778, 0.9151268], 'GSM3393503': [-0.7208419, -0.92774963, 2.0723180599999997, 0.13323009, -0.206316], 'GSM3393504': [0.58674955, 0.53776836, -1.4177436, 0.08122432, 1.2010365], 'GSM3393505': [0.035662174, 0.5369997, -2.5815987600000003, 0.32511508, -0.13713264], 'GSM3393506': [-0.088758945, 0.6998739, -3.4623508, 0.3340863, 0.03494072], 'GSM3393507': [0.12691832, 0.64743805, -4.8875618, 0.29584873, 0.053635597], 'GSM3393508': [0.20600271, 0.60525227, -4.4621973, 0.31017768, 0.1822033], 'GSM3393509': [0.78590536, 0.572505, 1.02886964, -0.15118802, -0.075992584], 'GSM3393510': [0.84085274, 0.32155704, -2.510457, -0.07825482, -0.2025361], 'GSM3393511': [0.80198574, 0.03553486, -0.9755090000000002, -0.30321276, -0.13455915], 'GSM3393512': [0.1604619, -0.19307852, -0.27777509999999994, -0.17000067, 0.08755493], 'GSM3393513': [0.21704626, -0.098829746, -0.21317049999999993, -0.32561076, -0.03494072], 'GSM3393514': [0.33046913, -0.35986662, -2.1921574, -0.50407183, -0.14380455], 'GSM3393515': [0.9540601, 0.13577461, -2.5504317, 0.031509757, -0.17405319], 'GSM3393516': [-0.048018932, -0.2030735, -5.8310852, 1.41897, -0.20944023], 'GSM3393517': [0.019157887, -0.1952858, -6.7030306, 1.2529734, -0.2085123], 'GSM3393518': [-1.2392125, -0.46589327, 4.5362944999999995, -0.76398003, -0.58863544], 'GSM3393519': [-1.1698852, -0.41910315, 3.841381174, -0.86528695, -0.40317822], 'GSM3393520': [-1.0839481, -0.516871, 3.887827826, -0.9265362, -0.4452448], 'GSM3393521': [-1.0274839, -0.3784132, 4.25438308, -0.72910845, -0.50286007], 'GSM3393522': [-0.27486825, 1.7698548, 10.7768375, 1.1431161, 2.1987486], 'GSM3393523': [-1.4494941, 0.5952289, 4.7863519199999995, -0.031509757, -6.070035], 'GSM3393524': [-0.25296688, 1.7917562, 7.17940616, 1.1650175, -2.7111485], 'GSM3393525': [-1.8236032, -1.995471, -2.59862992, 1.7294978, 0.28830147], 'GSM3393526': [-0.32608938, -1.5909972, 12.264716, -2.2177358, 0.13134003]}\n"
]
}
],
"source": [
"# 1. Identify the relevant columns from the gene annotation data\n",
"# From the preview, we can see that 'ID' contains the probe identifiers (like A_23_P100001) \n",
"# which match the gene expression data index, and 'GENE_SYMBOL' contains the gene symbols\n",
"\n",
"# 2. Get gene mapping dataframe by extracting the ID and GENE_SYMBOL columns\n",
"mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')\n",
"\n",
"# Print preview of the mapping dataframe\n",
"print(\"Gene mapping preview:\")\n",
"print(preview_df(mapping_df))\n",
"\n",
"# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n",
"# This handles the many-to-many relationship between probes and genes\n",
"gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
"\n",
"# Print the shape of the resulting gene expression dataframe\n",
"print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n",
"print(\"First few gene symbols after mapping:\")\n",
"print(gene_data.index[:10])\n",
"\n",
"# Preview the gene expression data\n",
"print(\"Gene expression data preview:\")\n",
"print(preview_df(gene_data))\n"
]
},
{
"cell_type": "markdown",
"id": "90f67ed5",
"metadata": {},
"source": [
"### Step 7: Data Normalization and Linking"
]
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalized gene data saved to ../../output/preprocess/Endometriosis/gene_data/GSE120103.csv\n",
"Reading previously saved clinical data\n",
"Clinical data preview:\n",
" 0 1 2 3\n",
"0 0.0 1.0 0.0 1.0\n",
"Transposed clinical features shape: (4, 1)\n",
"Transposed clinical features columns: Index(['Endometriosis'], dtype='object')\n",
"Linked data shape: (40, 17450)\n",
"Linked data columns preview: Index(['Endometriosis', 'A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2ML1', 'A4GALT',\n",
" 'A4GNT', 'AAAS', 'AACS'],\n",
" dtype='object')\n",
"Trait column 'Endometriosis' found in linked data\n",
"Endometriosis\n",
"0.0 2\n",
"1.0 2\n",
"Name: count, dtype: int64\n",
"Linked data shape after cleaning: (0, 1)\n",
"Quartiles for 'Endometriosis':\n",
" 25%: nan\n",
" 50% (Median): nan\n",
" 75%: nan\n",
"Min: nan\n",
"Max: nan\n",
"The distribution of the feature 'Endometriosis' in this dataset is fine.\n",
"\n",
"Abnormality detected in the cohort: GSE120103. Preprocessing failed.\n",
"Data was determined to be unusable and was not saved\n"
]
}
],
"source": [
"# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.\n",
"normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
"os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
"normalized_gene_data.to_csv(out_gene_data_file)\n",
"print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
"\n",
"# 2. Read the clinical data that was saved earlier\n",
"print(\"Reading previously saved clinical data\")\n",
"# First check if the clinical data file exists\n",
"if os.path.exists(out_clinical_data_file):\n",
" clinical_features_df = pd.read_csv(out_clinical_data_file)\n",
" print(\"Clinical data preview:\")\n",
" print(clinical_features_df.head())\n",
"else:\n",
" # If file doesn't exist, extract clinical features again\n",
" print(\"Clinical data file not found, extracting clinical features again\")\n",
" clinical_features_df = geo_select_clinical_features(\n",
" clinical_data, \n",
" trait=trait, \n",
" trait_row=trait_row, \n",
" convert_trait=convert_trait,\n",
" age_row=age_row,\n",
" convert_age=convert_age,\n",
" gender_row=gender_row,\n",
" convert_gender=convert_gender\n",
" )\n",
" os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
" clinical_features_df.to_csv(out_clinical_data_file)\n",
"\n",
"# Transpose the clinical_features_df to have samples as rows and features as columns\n",
"# This is necessary because the current format has features as rows, which is not what we want for linking\n",
"clinical_features_df_transposed = clinical_features_df.T\n",
"clinical_features_df_transposed = clinical_features_df_transposed.rename(columns={0: trait})\n",
"print(\"Transposed clinical features shape:\", clinical_features_df_transposed.shape)\n",
"print(\"Transposed clinical features columns:\", clinical_features_df_transposed.columns)\n",
"\n",
"# Now link the clinical and genetic data\n",
"linked_data = normalized_gene_data.T\n",
"linked_data = pd.concat([clinical_features_df_transposed, linked_data], axis=1)\n",
"print(\"Linked data shape:\", linked_data.shape)\n",
"print(\"Linked data columns preview:\", linked_data.columns[:10])\n",
"\n",
"# Check if trait column exists in linked data\n",
"if trait in linked_data.columns:\n",
" print(f\"Trait column '{trait}' found in linked data\")\n",
" print(linked_data[trait].value_counts())\n",
"else:\n",
" print(f\"Warning: '{trait}' column not found in linked data\")\n",
" # If trait column is missing, we have a problem\n",
" raise ValueError(f\"The trait column '{trait}' is missing in the linked data\")\n",
"\n",
"# Handle missing values in the linked data\n",
"linked_data_cleaned = handle_missing_values(linked_data, trait)\n",
"print(\"Linked data shape after cleaning:\", linked_data_cleaned.shape)\n",
"\n",
"# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.\n",
"is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data_cleaned, trait)\n",
"\n",
"# 5. Conduct quality check and save the cohort information.\n",
"is_usable = validate_and_save_cohort_info(\n",
" is_final=True, \n",
" cohort=cohort, \n",
" info_path=json_path, \n",
" is_gene_available=True, \n",
" is_trait_available=True, \n",
" is_biased=is_trait_biased, \n",
" df=unbiased_linked_data,\n",
" note=\"Dataset contains gene expression from endometrium of women with and without stage IV ovarian endometriosis, including both fertile and infertile subjects.\"\n",
")\n",
"\n",
"# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.\n",
"if is_usable:\n",
" os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
" unbiased_linked_data.to_csv(out_data_file)\n",
" print(f\"Linked data saved to {out_data_file}\")\n",
"else:\n",
" print(\"Data was determined to be unusable and was not saved\")"
]
}
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